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Validation of a wind misalignment observer using field test data

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  • Bottasso, C.L.
  • Riboldi, C.E.D.

Abstract

A previously described observer of wind misalignment is validated using field test data collected on the NREL CART3 wind turbine. The observer uses blade root bending moment 1P harmonics, computed using the transformation of Coleman and Feingold, to infer the rotor-equivalent relative wind direction. The observation model parameters are determined by a least squares fitting using recorded blade loads and met-mast measured wind direction and speed; a random sample consensus (RANSAC) algorithm is used to robustify the parameter estimation procedure while detecting outliers in the experimental samples. The observer is validated using an independent verification data set: recorded blade bending loads are fed to the observer and the estimated wind misalignment is compared to both the one provided by the met-mast vanes, assumed as the ground truth, and by an on-board nacelle-mounted wind vane. Results show that the rotor-equivalent wind misalignment estimates provided by the proposed observer are well correlated in the low frequency spectrum with the met-mast reference, and in general are in much better accordance with it than the on-board wind vane measurements.

Suggested Citation

  • Bottasso, C.L. & Riboldi, C.E.D., 2015. "Validation of a wind misalignment observer using field test data," Renewable Energy, Elsevier, vol. 74(C), pages 298-306.
    • Handle: RePEc:eee:renene:v:74:y:2015:i:c:p:298-306
    DOI: 10.1016/j.renene.2014.07.048
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    References listed on IDEAS

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    1. Bottasso, C.L. & Riboldi, C.E.D., 2014. "Estimation of wind misalignment and vertical shear from blade loads," Renewable Energy, Elsevier, vol. 62(C), pages 293-302.
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    Cited by:

    1. Kwansu Kim & Hyun-Gyu Kim & Yuan Song & Insu Paek, 2019. "Design and Simulation of an LQR-PI Control Algorithm for Medium Wind Turbine," Energies, MDPI, vol. 12(12), pages 1-18, June.
    2. Kim, Kwang-Ho & Bertelè, Marta & Bottasso, Carlo L., 2023. "Wind inflow observation from load harmonics via neural networks: A simulation and field study," Renewable Energy, Elsevier, vol. 204(C), pages 300-312.
    3. Bottasso, C.L. & Cacciola, S. & Schreiber, J., 2018. "Local wind speed estimation, with application to wake impingement detection," Renewable Energy, Elsevier, vol. 116(PA), pages 155-168.

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